Overview

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Academic contacts

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Offerings

DUBAI-ISC-TJD-FACE2FACE-2025-2025
DUBAI-ISC-TJD-FACE2FACE-2027-2027
DUBAI-ISC-TMD-FACE2FACE-2026-2026
DUBAI-ISC-TSD-FACE2FACE-2025-2025
DUBAI-ISC-TSD-FACE2FACE-2027-2027
KAPLAN-SGP-TJA-MIXEDMODE-2025-2025
KAPLAN-SGP-TJA-MIXEDMODE-2027-2027
KAPLAN-SGP-TMA-MIXEDMODE-2026-2026
KAPLAN-SGP-TSA-MIXEDMODE-2025-2025
KAPLAN-SGP-TSA-MIXEDMODE-2027-2027
MURDOCH-S2-FACE2FACE-2025-ONGOING
MURDOCH-S2-ONLINEFLEX-2025-ONGOING

Other learning activities

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Learning activities

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Learning outcomes

1.

Discuss the role of search algorithms and constraint satisfaction problems (CSPs) in solving real-world problems: Understand and articulate how search techniques and CSP methodologies can be applied to find solutions in various domains.

2.

Explain the theoretical foundations of search and CSP techniques, focusing on problem-solving using knowledge and reasoning: Delve into the principles and theories that underpin search algorithms and CSPs, with an emphasis on knowledge-based problem-solving.

3.

Evaluate AI systems, particularly graph databases, logical reasoning, and probabilistic models, for their suitability in specific applications and domains: Develop the ability to critically assess the effectiveness of graph databases, logic-based systems, and probabilistic models in diverse real-world contexts.

4.

Describe/discuss generative AI and its applications and analyse the social implications of AI: Explore the latest advancements in generative AI, its practical uses, and the ethical considerations and societal impacts of AI technologies.

Assessments

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